A Bounded Intensity Process Reliability Growth Model in a Bayes-Decision Framework

Abstract In this paper a Bounded Intensity Process (BIP) reliability growth model is used to analyze the failure data from repairable systems undergoing a Test-Find-Test growth program, in a Bayes-decision framework. Such an analysis is helpful in improving system reliability. Several identical copies of the equipment are put on test at each development stage. At the end of each stage, a decision between two alternative actions, viz., (a) to accept the current design of the system for mass production, or (b) to continue the development program, is made. The mean number of failures in a prefixed time interval is used to measure the system reliability at each testing stage, so that the decision process is based on the posterior distribution of this quantity and on specific loss functions that measure the economical consequences associated with each alternative action. A numerical example is used to illustrate the decision process.